The future of intelligent information retrieval is based on machine learning techniques such as Artificial Neural Network (ANN). ANN's ability to express non-linear relationships in data results in better classification and is best suited for information retrieval in applications such as pattern recognition, prediction, and classification.
The ANN technique attempts to emulate the architecture and information representation schemes of the human brain and its architecture depends on the goal to be achieved. The learning in ANN can be either supervised or unsupervised. In a supervised learning (SL) we assume what the result should be (like a teacher instructing a pupil). In this case we present the input, check what the output shows and then adjust the connection strengths between the input and output mapping until the correct output is given. This can be applied to all inputs until the network becomes as error free as possible. The SL method requires an output class declaration for each of the inputs.
Present SL methods require large numbers of sample inputs (data) to produce unbiased learning, prediction, and classification. When only sparse data is available, the SL methods require a greater number of iterations for convergence and this generally results in lower performance. Sparse data means available data is less than the required larger numbers of data to produce unbiased learning. The requirement of large amounts of data is generally a constraint. Current approaches use traditional techniques, including linear methods based on statistical techniques to compensate for the lack of large amounts of data when only sparse data is available. However, these methods can still result in biased (skewed) learning, classification, and prediction.
Therefore, there is a need in the art for unbiased learning, prediction, and classification using the SL method when only sparse data is available. Further, there is also a need in the art to increase performance and reduce the number of iterations required for a faster convergence by the SL method during machine learning when only sparse data is available.